Overview

Dataset statistics

Number of variables17
Number of observations165600
Missing cells151588
Missing cells (%)5.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.5 MiB
Average record size in memory136.0 B

Variable types

NUM12
CAT5

Reproduction

Analysis started2020-11-16 03:27:33.269262
Analysis finished2020-11-16 03:28:24.262472
Duration50.99 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Zipcode has a high cardinality: 33120 distinct values High cardinality
State has a high cardinality: 51 distinct values High cardinality
City has a high cardinality: 14740 distinct values High cardinality
Metro has a high cardinality: 861 distinct values High cardinality
CountyName has a high cardinality: 1759 distinct values High cardinality
Year is highly correlated with df_index and 5 other fieldsHigh correlation
df_index is highly correlated with Year and 5 other fieldsHigh correlation
int_rate is highly correlated with df_index and 2 other fieldsHigh correlation
med_hIncome is highly correlated with df_index and 4 other fieldsHigh correlation
uspop_growth is highly correlated with int_rateHigh correlation
unemplt_rate is highly correlated with df_index and 4 other fieldsHigh correlation
newHouse_starts is highly correlated with df_index and 4 other fieldsHigh correlation
resConstruct_spending is highly correlated with df_index and 4 other fieldsHigh correlation
RentPrice has 12081 (7.3%) missing values Missing
SizeRank has 16910 (10.2%) missing values Missing
State has 16910 (10.2%) missing values Missing
City has 16910 (10.2%) missing values Missing
Metro has 51880 (31.3%) missing values Missing
CountyName has 16910 (10.2%) missing values Missing
HomePrice has 19987 (12.1%) missing values Missing
Zipcode is uniformly distributed Uniform
df_index has unique values Unique
Vacancy_Rate% has 8967 (5.4%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count165600
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182159.5
Minimum99360
Maximum264959
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:24.477233image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum99360
5-th percentile107639.95
Q1140759.75
median182159.5
Q3223559.25
95-th percentile256679.05
Maximum264959
Range165599
Interquartile range (IQR)82799.5

Descriptive statistics

Standard deviation47804.74663
Coefficient of variation (CV)0.2624334532
Kurtosis-1.2
Mean182159.5
Median Absolute Deviation (MAD)41400
Skewness0
Sum3.01656132e+10
Variance2285293800
2020-11-15T22:28:24.628538image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2641911< 0.1%
 
1202081< 0.1%
 
1324581< 0.1%
 
1386011< 0.1%
 
1365521< 0.1%
 
1590791< 0.1%
 
1570301< 0.1%
 
1631731< 0.1%
 
1611241< 0.1%
 
1508831< 0.1%
 
Other values (165590)165590> 99.9%
 
ValueCountFrequency (%) 
993601< 0.1%
 
993611< 0.1%
 
993621< 0.1%
 
993631< 0.1%
 
993641< 0.1%
 
ValueCountFrequency (%) 
2649591< 0.1%
 
2649581< 0.1%
 
2649571< 0.1%
 
2649561< 0.1%
 
2649551< 0.1%
 

Zipcode
Categorical

HIGH CARDINALITY
UNIFORM

Distinct count33120
Unique (%)20.0%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
17021
 
5
86511
 
5
58051
 
5
60901
 
5
08550
 
5
Other values (33115)
165575
ValueCountFrequency (%) 
170215< 0.1%
 
865115< 0.1%
 
580515< 0.1%
 
609015< 0.1%
 
085505< 0.1%
 
922505< 0.1%
 
606315< 0.1%
 
173605< 0.1%
 
604785< 0.1%
 
500295< 0.1%
 
Other values (33110)165550> 99.9%
 
2020-11-15T22:28:25.968965image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

RentPrice
Real number (ℝ≥0)

MISSING

Distinct count99354
Unique (%)64.7%
Missing12081
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean1068.026284140725
Minimum19.960000000000036
Maximum5620.320000000002
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:26.169945image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum19.96
5-th percentile568.139
Q1800.07
median964.99
Q31220.73
95-th percentile1892.3805
Maximum5620.32
Range5600.36
Interquartile range (IQR)420.66

Descriptive statistics

Standard deviation446.1696767
Coefficient of variation (CV)0.4177515884
Kurtosis10.77533879
Mean1068.026284
Median Absolute Deviation (MAD)197
Skewness2.339095842
Sum163962327.1
Variance199067.3804
2020-11-15T22:28:26.311061image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1068.851960.1%
 
1339.991900.1%
 
1094.961890.1%
 
1064.991880.1%
 
1011.561870.1%
 
1068.3251870.1%
 
1343.851820.1%
 
839.991790.1%
 
1286.561770.1%
 
1343.3251770.1%
 
Other values (99344)15166791.6%
 
(Missing)120817.3%
 
ValueCountFrequency (%) 
19.967< 0.1%
 
94.968< 0.1%
 
103.291< 0.1%
 
133.951< 0.1%
 
139.41< 0.1%
 
ValueCountFrequency (%) 
5620.325< 0.1%
 
5619.7952< 0.1%
 
5616.463< 0.1%
 
5563.032< 0.1%
 
5410.421< 0.1%
 

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.0
Minimum2014
Maximum2018
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:26.462202image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12015
median2016
Q32017
95-th percentile2018
Maximum2018
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.414217832
Coefficient of variation (CV)0.0007014969407
Kurtosis-1.300003019
Mean2016
Median Absolute Deviation (MAD)1
Skewness0
Sum333849600
Variance2.000012077
2020-11-15T22:28:26.621038image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20183312020.0%
 
20173312020.0%
 
20163312020.0%
 
20153312020.0%
 
20143312020.0%
 
ValueCountFrequency (%) 
20143312020.0%
 
20153312020.0%
 
20163312020.0%
 
20173312020.0%
 
20183312020.0%
 
ValueCountFrequency (%) 
20183312020.0%
 
20173312020.0%
 
20163312020.0%
 
20153312020.0%
 
20143312020.0%
 

SizeRank
Real number (ℝ≥0)

MISSING

Distinct count11054
Unique (%)7.4%
Missing16910
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean15646.706806106666
Minimum0.0
Maximum34430.0
Zeros5
Zeros (%)< 0.1%
Memory size1.3 MiB
2020-11-15T22:28:26.792581image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1503
Q17531
median15164
Q323514
95-th percentile31180
Maximum34430
Range34430
Interquartile range (IQR)15983

Descriptive statistics

Standard deviation9424.136486
Coefficient of variation (CV)0.6023079874
Kurtosis-1.122785048
Mean15646.70681
Median Absolute Deviation (MAD)7971.5
Skewness0.1321161222
Sum2326508835
Variance88814348.51
2020-11-15T22:28:26.943224image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
299641800.1%
 
320621750.1%
 
305451750.1%
 
296851550.1%
 
308921550.1%
 
289041550.1%
 
280971550.1%
 
294391500.1%
 
300961450.1%
 
305041450.1%
 
Other values (11044)14710088.8%
 
(Missing)1691010.2%
 
ValueCountFrequency (%) 
05< 0.1%
 
15< 0.1%
 
25< 0.1%
 
35< 0.1%
 
45< 0.1%
 
ValueCountFrequency (%) 
344301150.1%
 
3432280< 0.1%
 
3430215< 0.1%
 
342725< 0.1%
 
342585< 0.1%
 

State
Categorical

HIGH CARDINALITY
MISSING

Distinct count51
Unique (%)< 0.1%
Missing16910
Missing (%)10.2%
Memory size1.3 MiB
TX
 
8800
NY
 
8490
CA
 
8315
PA
 
8155
IL
 
6345
Other values (46)
108585
ValueCountFrequency (%) 
TX88005.3%
 
NY84905.1%
 
CA83155.0%
 
PA81554.9%
 
IL63453.8%
 
OH58003.5%
 
FL47152.8%
 
MI46902.8%
 
MO46452.8%
 
IA46152.8%
 
Other values (41)8412050.8%
 
(Missing)1691010.2%
 
2020-11-15T22:28:27.982243image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.102113527
Min length2

City
Categorical

HIGH CARDINALITY
MISSING

Distinct count14740
Unique (%)9.9%
Missing16910
Missing (%)10.2%
Memory size1.3 MiB
New York
 
855
Houston
 
535
Los Angeles
 
500
San Antonio
 
280
Chicago
 
275
Other values (14735)
146245
ValueCountFrequency (%) 
New York8550.5%
 
Houston5350.3%
 
Los Angeles5000.3%
 
San Antonio2800.2%
 
Chicago2750.2%
 
Springfield2700.2%
 
Dallas2600.2%
 
Columbus2550.2%
 
Kansas City2500.2%
 
Philadelphia2450.1%
 
Other values (14730)14496587.5%
 
(Missing)1691010.2%
 
2020-11-15T22:28:29.055618image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length30
Median length8
Mean length8.448641304
Min length3

Metro
Categorical

HIGH CARDINALITY
MISSING

Distinct count861
Unique (%)0.8%
Missing51880
Missing (%)31.3%
Memory size1.3 MiB
New York-Newark-Jersey City
 
4635
Chicago-Naperville-Elgin
 
1910
Los Angeles-Long Beach-Anaheim
 
1810
Philadelphia-Camden-Wilmington
 
1770
Washington-Arlington-Alexandria
 
1595
Other values (856)
102000
ValueCountFrequency (%) 
New York-Newark-Jersey City46352.8%
 
Chicago-Naperville-Elgin19101.2%
 
Los Angeles-Long Beach-Anaheim18101.1%
 
Philadelphia-Camden-Wilmington17701.1%
 
Washington-Arlington-Alexandria15951.0%
 
Pittsburgh15901.0%
 
Boston-Cambridge-Newton13800.8%
 
Dallas-Fort Worth-Arlington13200.8%
 
Houston-The Woodlands-Sugar Land11800.7%
 
Minneapolis-St. Paul-Bloomington11500.7%
 
Other values (851)9538057.6%
 
(Missing)5188031.3%
 
2020-11-15T22:28:30.081336image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length42
Median length9
Mean length12.30697464
Min length3

CountyName
Categorical

HIGH CARDINALITY
MISSING

Distinct count1759
Unique (%)1.2%
Missing16910
Missing (%)10.2%
Memory size1.3 MiB
Washington County
 
1780
Jefferson County
 
1625
Los Angeles County
 
1375
Franklin County
 
1325
Montgomery County
 
1325
Other values (1754)
141260
ValueCountFrequency (%) 
Washington County17801.1%
 
Jefferson County16251.0%
 
Los Angeles County13750.8%
 
Franklin County13250.8%
 
Montgomery County13250.8%
 
Jackson County11650.7%
 
Orange County11000.7%
 
Marion County9100.5%
 
Wayne County8800.5%
 
Monroe County8800.5%
 
Other values (1749)13632582.3%
 
(Missing)1691010.2%
 
2020-11-15T22:28:31.107813image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length29
Median length14
Mean length13.07388285
Min length3

HomePrice
Real number (ℝ≥0)

MISSING

Distinct count142642
Unique (%)98.0%
Missing19987
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean194823.4147800677
Minimum11860.83
Maximum6141945.92
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:31.629132image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum11860.83
5-th percentile51255.216
Q190546.75
median139991.42
Q3225666.83
95-th percentile512356.518
Maximum6141945.92
Range6130085.09
Interquartile range (IQR)135120.08

Descriptive statistics

Standard deviation201176.0974
Coefficient of variation (CV)1.032607388
Kurtosis66.01927228
Mean194823.4148
Median Absolute Deviation (MAD)59113.25
Skewness5.768940617
Sum2.83688219e+10
Variance4.047182217e+10
2020-11-15T22:28:31.785177image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
57537.174< 0.1%
 
75169.834< 0.1%
 
50318.583< 0.1%
 
57930.53< 0.1%
 
103050.083< 0.1%
 
64628.333< 0.1%
 
132820.53< 0.1%
 
495793< 0.1%
 
59689.423< 0.1%
 
54673.673< 0.1%
 
Other values (142632)14558187.9%
 
(Missing)1998712.1%
 
ValueCountFrequency (%) 
11860.831< 0.1%
 
12147.51< 0.1%
 
12309.921< 0.1%
 
13387.331< 0.1%
 
13546.671< 0.1%
 
ValueCountFrequency (%) 
6141945.921< 0.1%
 
5373670.921< 0.1%
 
5197037.171< 0.1%
 
4928414.671< 0.1%
 
4771183.921< 0.1%
 

Vacancy_Rate%
Real number (ℝ≥0)

ZEROS

Distinct count116581
Unique (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.905033346462375
Minimum0.0
Maximum100.0
Zeros8967
Zeros (%)5.4%
Memory size1.3 MiB
2020-11-15T22:28:31.990460image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.014507465
median13.0191052
Q322.92878031
95-th percentile53.21844737
Maximum100
Range100
Interquartile range (IQR)15.91427285

Descriptive statistics

Standard deviation16.57333207
Coefficient of variation (CV)0.9256241947
Kurtosis4.447530561
Mean17.90503335
Median Absolute Deviation (MAD)7.094430238
Skewness1.932858015
Sum2965073.522
Variance274.675336
2020-11-15T22:28:32.127363image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
089675.4%
 
1004320.3%
 
251990.1%
 
201950.1%
 
33.333333331800.1%
 
16.666666671610.1%
 
14.285714291330.1%
 
12.51310.1%
 
501240.1%
 
11.111111111110.1%
 
Other values (116571)15496793.6%
 
ValueCountFrequency (%) 
089675.4%
 
0.022727272731< 0.1%
 
0.11148272021< 0.1%
 
0.12484394511< 0.1%
 
0.14025245441< 0.1%
 
ValueCountFrequency (%) 
1004320.3%
 
99.839743591< 0.1%
 
99.717912551< 0.1%
 
99.653379551< 0.1%
 
99.573863641< 0.1%
 

int_rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.325
Minimum0.75
Maximum2.458333333333333
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:32.276716image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile0.75
Q10.7708333333
median1.020833333
Q31.625
95-th percentile2.458333333
Maximum2.458333333
Range1.708333333
Interquartile range (IQR)0.8541666667

Descriptive statistics

Standard deviation0.6487989226
Coefficient of variation (CV)0.4896595642
Kurtosis-0.8891515948
Mean1.325
Median Absolute Deviation (MAD)0.2708333333
Skewness0.8013670211
Sum219420
Variance0.4209400419
2020-11-15T22:28:32.432742image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.0208333333312020.0%
 
0.77083333333312020.0%
 
2.4583333333312020.0%
 
1.6253312020.0%
 
0.753312020.0%
 
ValueCountFrequency (%) 
0.753312020.0%
 
0.77083333333312020.0%
 
1.0208333333312020.0%
 
1.6253312020.0%
 
2.4583333333312020.0%
 
ValueCountFrequency (%) 
2.4583333333312020.0%
 
1.6253312020.0%
 
1.0208333333312020.0%
 
0.77083333333312020.0%
 
0.753312020.0%
 

med_hIncome
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61994.2
Minimum58001.0
Maximum64324.0
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:32.588767image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum58001
5-th percentile58001
Q160987
median62898
Q363761
95-th percentile64324
Maximum64324
Range6323
Interquartile range (IQR)2774

Descriptive statistics

Standard deviation2294.631202
Coefficient of variation (CV)0.03701364325
Kurtosis-0.8706176326
Mean61994.2
Median Absolute Deviation (MAD)1426
Skewness-0.7581061267
Sum1.026623952e+10
Variance5265332.355
2020-11-15T22:28:32.741550image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
643243312020.0%
 
637613312020.0%
 
628983312020.0%
 
609873312020.0%
 
580013312020.0%
 
ValueCountFrequency (%) 
580013312020.0%
 
609873312020.0%
 
628983312020.0%
 
637613312020.0%
 
643243312020.0%
 
ValueCountFrequency (%) 
643243312020.0%
 
637613312020.0%
 
628983312020.0%
 
609873312020.0%
 
580013312020.0%
 

uspop_growth
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6656347077111499
Minimum0.5223373578996761
Maximum0.730641178178307
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:32.895068image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0.5223373579
5-th percentile0.5223373579
Q10.6310078932
median0.7166694134
Q30.7275176958
95-th percentile0.7306411782
Maximum0.7306411782
Range0.2083038203
Interquartile range (IQR)0.09650980259

Descriptive statistics

Standard deviation0.08049003536
Coefficient of variation (CV)0.1209222332
Kurtosis-0.7966578346
Mean0.6656347077
Median Absolute Deviation (MAD)0.01397176475
Skewness-0.8972530783
Sum110229.1076
Variance0.006478645793
2020-11-15T22:28:33.028201image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.63100789323312020.0%
 
0.71666941343312020.0%
 
0.73064117823312020.0%
 
0.52233735793312020.0%
 
0.72751769583312020.0%
 
ValueCountFrequency (%) 
0.52233735793312020.0%
 
0.63100789323312020.0%
 
0.71666941343312020.0%
 
0.72751769583312020.0%
 
0.73064117823312020.0%
 
ValueCountFrequency (%) 
0.73064117823312020.0%
 
0.72751769583312020.0%
 
0.71666941343312020.0%
 
0.63100789323312020.0%
 
0.52233735793312020.0%
 

unemplt_rate
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.908333333333333
Minimum3.8916666666666666
Maximum6.158333333333332
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:33.171690image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3.891666667
5-th percentile3.891666667
Q14.341666667
median4.875
Q35.275
95-th percentile6.158333333
Maximum6.158333333
Range2.266666667
Interquartile range (IQR)0.9333333333

Descriptive statistics

Standard deviation0.7813829039
Coefficient of variation (CV)0.1591951587
Kurtosis-1.051948045
Mean4.908333333
Median Absolute Deviation (MAD)0.5333333333
Skewness0.3226278116
Sum812820
Variance0.6105592425
2020-11-15T22:28:33.308426image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5.2753312020.0%
 
4.3416666673312020.0%
 
6.1583333333312020.0%
 
4.8753312020.0%
 
3.8916666673312020.0%
 
ValueCountFrequency (%) 
3.8916666673312020.0%
 
4.3416666673312020.0%
 
4.8753312020.0%
 
5.2753312020.0%
 
6.1583333333312020.0%
 
ValueCountFrequency (%) 
6.1583333333312020.0%
 
5.2753312020.0%
 
4.8753312020.0%
 
4.3416666673312020.0%
 
3.8916666673312020.0%
 

newHouse_starts
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1147.85
Minimum1000.25
Maximum1248.25
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:33.459056image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1000.25
5-th percentile1000.25
Q11106.75
median1176.583333
Q31207.416667
95-th percentile1248.25
Maximum1248.25
Range248
Interquartile range (IQR)100.6666667

Descriptive statistics

Standard deviation87.09667188
Coefficient of variation (CV)0.07587809547
Kurtosis-0.9412115637
Mean1147.85
Median Absolute Deviation (MAD)69.83333333
Skewness-0.6168959352
Sum190083960
Variance7585.830253
2020-11-15T22:28:33.597666image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1000.253312020.0%
 
1176.5833333312020.0%
 
1207.4166673312020.0%
 
1248.253312020.0%
 
1106.753312020.0%
 
ValueCountFrequency (%) 
1000.253312020.0%
 
1106.753312020.0%
 
1176.5833333312020.0%
 
1207.4166673312020.0%
 
1248.253312020.0%
 
ValueCountFrequency (%) 
1248.253312020.0%
 
1207.4166673312020.0%
 
1176.5833333312020.0%
 
1106.753312020.0%
 
1000.253312020.0%
 

resConstruct_spending
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483455.61666666646
Minimum382868.33333333326
Maximum564448.75
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-11-15T22:28:33.746215image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum382868.3333
5-th percentile382868.3333
Q1438118.3333
median485822.5
Q3546020.1667
95-th percentile564448.75
Maximum564448.75
Range181580.4167
Interquartile range (IQR)107901.8333

Descriptive statistics

Standard deviation67310.05916
Coefficient of variation (CV)0.139226967
Kurtosis-1.392824449
Mean483455.6167
Median Absolute Deviation (MAD)60197.66667
Skewness-0.2195061288
Sum8.006025012e+10
Variance4530644065
2020-11-15T22:28:33.903243image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
546020.16673312020.0%
 
485822.53312020.0%
 
438118.33333312020.0%
 
564448.753312020.0%
 
382868.33333312020.0%
 
ValueCountFrequency (%) 
382868.33333312020.0%
 
438118.33333312020.0%
 
485822.53312020.0%
 
546020.16673312020.0%
 
564448.753312020.0%
 
ValueCountFrequency (%) 
564448.753312020.0%
 
546020.16673312020.0%
 
485822.53312020.0%
 
438118.33333312020.0%
 
382868.33333312020.0%
 

Interactions

2020-11-15T22:27:45.379515image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:46.604822image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:46.839638image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:47.081884image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:47.318038image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:47.558557image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:47.784864image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:48.018562image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:48.264800image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:48.492281image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:48.719574image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:48.954574image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:49.216471image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:49.463745image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:49.722430image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:49.962132image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:50.192858image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:50.429778image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:50.667954image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:50.902928image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:51.122663image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:51.333174image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:51.551532image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:51.767353image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:52.006337image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:52.272880image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:52.529168image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:52.799922image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:53.062331image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:53.310877image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:53.690354image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:53.944586image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:54.207946image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:54.457853image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:54.707920image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:54.955839image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:55.228977image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:55.470502image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:55.713494image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:55.962093image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:56.200299image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:56.435176image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:56.655802image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:56.895592image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:57.139245image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:57.370473image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:57.601929image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:57.827164image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:58.071122image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:58.301187image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:58.535904image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:58.766676image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:58.998304image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:59.227785image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:59.449346image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:59.689200image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:27:59.914254image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:00.126173image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:00.341219image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:00.596794image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:00.857277image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:01.091540image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:01.317861image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:01.558621image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:01.785019image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:02.010738image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:02.389050image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:02.613996image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:02.833835image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:03.034541image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:03.244261image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:03.464030image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:03.700042image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:03.943712image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:04.182384image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:04.436249image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:04.692273image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:04.941006image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:05.174110image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:05.417126image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:05.675632image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:05.906131image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:06.138474image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:06.370683image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:06.634091image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:06.871905image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:07.102895image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:07.364498image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:07.610879image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:07.847840image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:08.069521image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:08.302945image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:08.549391image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:08.774784image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:09.000691image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:09.230680image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:09.480554image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:09.702311image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:09.928651image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:10.190384image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:10.407359image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:10.624871image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:10.842389image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:11.058623image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:11.278192image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:11.491241image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:11.700452image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:11.916724image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:12.155446image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:12.388543image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:12.613672image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:12.857075image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:13.369031image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:13.636305image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:13.880104image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:14.112752image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:14.337280image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:14.554275image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:14.777007image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:14.986656image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:15.228809image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:15.466540image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:15.707170image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:15.942191image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:16.178651image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:16.410845image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:16.628391image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:16.870721image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:17.098396image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:17.321502image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:17.540767image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:17.769766image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:18.009832image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:18.258847image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:18.509979image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:18.772438image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:19.026223image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:19.288253image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:19.534324image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:19.801927image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:20.051211image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:20.284549image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:20.529948image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:20.777253image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-11-15T22:28:34.073860image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-15T22:28:34.374299image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-15T22:28:34.876275image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-15T22:28:35.195103image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-15T22:28:21.476499image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:22.327055image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:23.421751image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-11-15T22:28:23.808654image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

df_indexZipcodeRentPriceYearSizeRankStateCityMetroCountyNameHomePriceVacancy_Rate%int_ratemed_hIncomeuspop_growthunemplt_ratenewHouse_startsresConstruct_spending
09936001062989.0520148993.0MANorthamptonSpringfieldHampshire County260977.175.3333330.7558001.00.7275186.1583331000.25382868.333333
19936101063NaN201434430.0MANorthamptonSpringfieldHampshire County554513.420.0000000.7558001.00.7275186.1583331000.25382868.333333
299362010661344.96201428699.0MAHatfieldSpringfieldHampshire County294024.330.0000000.7558001.00.7275186.1583331000.25382868.333333
39936301068999.00201419818.0MAOakhamWorcesterWorcester County242939.005.4652880.7558001.00.7275186.1583331000.25382868.333333
49936401069657.39201410351.0MAPalmerSpringfieldHampden County181901.608.5577180.7558001.00.7275186.1583331000.25382868.333333
59936501070752.10201426235.0MAPlainfieldSpringfieldHampshire County217112.7525.6024100.7558001.00.7275186.1583331000.25382868.333333
69936601071986.63201421453.0MARussellSpringfieldHampden County194616.338.9256200.7558001.00.7275186.1583331000.25382868.333333
799367010721530.33201421068.0MAShutesburyGreenfield TownFranklin County232787.5817.8851170.7558001.00.7275186.1583331000.25382868.333333
899368010731106.99201412153.0MASouthamptonSpringfieldHampshire County277385.582.1648630.7558001.00.7275186.1583331000.25382868.333333
999369010741234.572014NaNNaNNaNNaNNaNNaN14.2857140.7558001.00.7275186.1583331000.25382868.333333

Last rows

df_indexZipcodeRentPriceYearSizeRankStateCityMetroCountyNameHomePriceVacancy_Rate%int_ratemed_hIncomeuspop_growthunemplt_ratenewHouse_startsresConstruct_spending
165590264950981341909.58201828159.0WASeattleSeattle-Tacoma-BellevueKing County438970.0013.5802472.45833364324.00.5223373.8916671248.25564448.75
16559126495198174NaN2018NaNNaNNaNNaNNaNNaN0.0000002.45833364324.00.5223373.8916671248.25564448.75
16559226495298222NaN201830981.0WAOlgaNaNSan Juan County580646.5883.4710742.45833364324.00.5223373.8916671248.25564448.75
165593264953982331413.8920187640.0WABurlingtonMount Vernon-AnacortesSkagit County317426.754.8537652.45833364324.00.5223373.8916671248.25564448.75
165594264954982431302.942018NaNNaNNaNNaNNaNNaN57.2932332.45833364324.00.5223373.8916671248.25564448.75
165595264955982791059.87201823400.0WAOlgaNaNSan Juan County552805.4251.2195122.45833364324.00.5223373.8916671248.25564448.75
16559626495698280993.85201825265.0WAEastsoundNaNSan Juan County678499.0051.3292432.45833364324.00.5223373.8916671248.25564448.75
165597264957983111533.5020184981.0WABremertonBremerton-SilverdaleKitsap County314320.836.5401622.45833364324.00.5223373.8916671248.25564448.75
16559826495898326778.99201826185.0WAClallam BayPort AngelesClallam County150193.1728.5377362.45833364324.00.5223373.8916671248.25564448.75
165599264959983321840.8620186759.0WAGig HarborSeattle-Tacoma-BellevuePierce County535136.757.3400772.45833364324.00.5223373.8916671248.25564448.75